3D Solar Coronal Loop Reconstructions with Machine Learning

Numerical simulations of vertical oscillations of a solar coronal loop

We consider the impulsive excitation of fast vertical kink standing waves in a solar coronal loop that is embedded in a potential arcade. The two-dimensional numerical model we implement includes the effects of field line curvature and nonlinearity on the excitation and damping of standing fast magnetosonic waves. The results of the numerical simulations reveal wave signatures which are charact...

Classification of Solar Wind with Machine Learning

We present a four-category classification algorithm for the solar wind, based on Gaussian Process. The four categories are the ones previously adopted in Xu and Borovsky [2015]: ejecta, coronal hole origin plasma, streamer belt origin plasma, and sector reversal origin plasma. The algorithm is trained and tested on a labeled portion of the OMNI dataset. It uses seven inputs: the solar wind spee...

Solar Wind Forecasting with Coronal Holes

An empirical model for forecasting solar wind speed related geomagnetic events is presented here. The model is based on the estimated location and size of solar coronal holes. This method differs from models that are based on photospheric magnetograms (e.g., Wang-Sheeley model) to estimate the open field line configuration. Rather than requiring the use of a full magnetic synoptic map, the meth...

Quantum Loop Topography for Machine Learning.

Despite rapidly growing interest in harnessing machine learning in the study of quantum many-body systems, training neural networks to identify quantum phases is a nontrivial challenge. The key challenge is in efficiently extracting essential information from the many-body Hamiltonian or wave function and turning the information into an image that can be fed into a neural network. When targetin...

Reconstruction of Solar Coronal Linear Force-Free Magnetic Field